Introduction
In the Swiss canton of St. Gallen, near the northern banks of Lake Zurich, is a village named Bollingen. In 1922, the psychiatrist Carl Jung chose this spot to begin building a retreat. He began with a basic two-story stone house he called the Tower. He chosen this place for focusing. Setting mind into one direction towards to his destination. Focused how to achieve it. He builds new ideas. We are in this world today because of the men like him and the men like him can think when they focus and for focusing deep work is initial to understand.
Deep work is really important to understand in this fast going world. It shows us how to develop yourself in this way that you can learn and think anything you want according the world requires. It improves your ability to change according to world.
Carl Jung went on to become one of the most influential thinkers of the twentieth century.
Moving forward in history, consider the screenwriter and director Woody Allen. In the forty-four-year period between 1969 and 2013, Woody Allen wrote and directed forty-four films that received twenty-three Academy Award nominations—an absurd rate of artistic productivity. Throughout this period, Allen never owned a computer, instead completing all his writing, free from electronic distraction, on a German Olympia SM3 manual typewriter.
Deep work, of course, is not limited to the historical or technophobic. Microsoft CEO Bill Gates famously conducted “Think Weeks” twice a year, during which he would isolate himself (often in a lakeside cottage) to do nothing but read and think big thoughts.
The firm that hired Benn produced reports for banks involved in complex deals. (“It was about as interesting as it sounds,” Benn joked in one of our interviews.) The report creation process required hours of manual manipulation of data in a series of Excel spreadsheets. When he first arrived, it took Benn up to six hours per report to finish this stage (the most efficient veterans at the firm could complete this task in around half the time). This didn’t sit well with Benn. “The way it was taught to me, the process seemed clunky and manually intensive,” Benn recalls. He knew that Excel has a feature called macros that allows users to automate common tasks. Benn read articles on the topic and soon put together a new worksheet, wired up with a series of these macros that could take the six-hour process of manual data manipulation and replace it, essentially, with a button click. A report-writing process that originally took him a full workday could now be reduced to less than an hour. Benn is a smart guy. He graduated from an elite college (the University of Virginia) with a degree in economics, and like many in his situation he had ambitions for his career. It didn’t take him long to realize that these ambitions would be thwarted so long as his main professional skills could be captured in an Excel macro. He decided, therefore, he needed to increase his value to the world. After a period of research, Benn reached a conclusion: He would, he declared to his family, quit his job as a human spreadsheet and become a computer programmer. As is often the case with such grand plans, however, there was a hitch: Jason Benn had no idea how to write code.
As a computer scientist I can confirm an obvious point: Programming computers is hard. Most new developers dedicate a four-year college education to learning the ropes before their first job—and even then, competition for the best spots is fierce. Jason Benn didn’t have this time. After his Excel epiphany, he quit his job at the financial firm and moved home to prepare for his next step. His parents were happy he had a plan, but they weren’t happy about the idea that this return home might be long-term. Benn needed to learn a hard skill, and needed to do so fast. It’s here that Benn ran into the same problem that holds back many knowledge workers from navigating into more explosive career trajectories. Learning something complex like computer programming requires intense uninterrupted concentration on cognitively demanding concepts—the type of concentration that drove Carl Jung to the woods surrounding Lake Zurich. This task, in other words, is an act of deep work. Most knowledge workers, however, as I argued earlier in this introduction, have lost their ability to perform deep work. Benn was no exception to this trend. “I was always getting on the Internet and checking my e-mail; I couldn’t stop myself; it was a compulsion,” Benn said, describing himself during the period leading up to his quitting his finance job. To emphasize his difficulty with depth, Benn told me about a project that a supervisor at the finance firm once brought to him. “They wanted me to write a business plan,” he explained. Benn didn’t know how to write a business plan, so he decided he would find and read five different existing plans—comparing and contrasting them to understand what was needed. This was a good idea, but Benn had a problem: “I couldn’t stay focused.” There were days during this period, he now admits, when he spent almost every minute (“98 percent of my time”) surfing the Web. The business plan project—a chance to distinguish himself early in his career—fell to the wayside. By the time he quit, Benn was well aware of his difficulties with deep work, so when he dedicated himself to learning how to code, he knew he had to simultaneously teach his mind how to go deep. His method was drastic but effective. “I locked myself in a room with no computer: just textbooks, notecards, and a highlighter.” He would highlight the computer programming textbooks, transfer the ideas to notecards, and then practice them out loud. These periods free from electronic distraction were hard at first, but Benn gave himself no other option: He had to learn this material, and he made sure there was nothing in that room to distract him. Over time, however, he got better at concentrating, eventually getting to a point where he was regularly clocking five or more disconnected hours per day in the room, focused without distraction on learning this hard new skill. “I probably read something like eighteen books on the topic by the time I was done,” he recalls.
After two months locked away studying, Benn attended the notoriously difficult Dev Bootcamp: a hundred-hour-a-week crash course in Web application programming. (While researching the program, Benn found a student with a PhD from Princeton who had described Dev as “the hardest thing I’ve ever done in my life.”) Given both his preparation and his newly honed ability for deep work, Benn excelled. “Some people show up not prepared,” he said. “They can’t focus. They can’t learn quickly.” Only half the students who started the program with Benn ended up graduating on time. Benn not only graduated, but was also the top student in his class.
The deep work paid off. Benn quickly landed a job as a developer at a San Francisco tech start-up with $25 million in venture funding and its pick of employees. When Benn quit his job as a financial consultant, only half a year earlier, he was making $40,000 a year. His new job as a computer developer paid $100,000—an amount that can continue to grow, essentially without limit in the Silicon Valley market, along with his skill level.
Part 1 (The Idea)
Chapter One (Deep Work is Valuable)
Academic award winner David Heinemeier Hansson, a computer programming star who created the Ruby on Rails website development framework, which currently provides the foundation for some of the Web’s most popular destinations, including Twitter and Hulu.
Our second and final example of a clear winner in our economy is John Doerr, a general partner in the famed Silicon Valley venture capital fund Kleiner Perkins Caufield & Byers. Doerr helped fund many of the key companies fueling the current technological revolution, including Twitter, Google, Amazon, Netscape, and Sun Microsystems. The return on these investments has been astronomical: Doerr’s net worth, as of this writing, is more than $3 billion.
“Our technologies are racing ahead but many of our skills and organizations are lagging behind.” For many workers, this lag predicts bad news. As intelligent machines improve, and the gap between machine and human abilities shrinks, employers are becoming increasingly likely to hire “new machines” instead of “new people.” And when only a human will do, improvements in communications and collaboration technology are making remote work easier than ever before, motivating companies to outsource key roles to stars—leaving the local talent pool underemployed.
It’s to these three groups that Silver, Hansson, and Doerr happen to belong. Let’s touch on each of these groups in turn to better understand why they’re suddenly so valuable.
The High Skilled Workers
Brynjolfsson and McAfee call the group personified by Nate Silver the “high-skilled” workers. Advances such as robotics and voice recognition are automating many low-skilled positions, but as these economists emphasize, “other technologies like data visualization, analytics, high speed communications, and rapid prototyping have augmented the contributions of more abstract and data-driven reasoning, increasing the values of these jobs.” In other words, those with the oracular ability to work with and tease valuable results out of increasingly complex machines will thrive. Tyler Cowen summarizes this reality more bluntly: “The key question will be: are you good at working with intelligent machines or not?”
Nate Silver, of course, with his comfort in feeding data into large databases, then siphoning it out into his mysterious Monte Carlo simulations, is the epitome of the high-skilled worker. Intelligent machines are not an obstacle to Silver’s success, but instead provide its precondition.
The Superstars
The ace programmer David Heinemeier Hansson provides an example of the second group that Brynjolfsson and McAfee predict will thrive in our new economy: “superstars.” High-speed data networks and collaboration tools like e-mail and virtual meeting software have destroyed regionalism in many sectors of knowledge work. It no longer makes sense, for example, to hire a full-time programmer, put aside office space, and pay benefits, when you can instead pay one of the world’s best programmers, like Hansson, for just enough time to complete the project at hand. In this scenario, you’ll probably get a better result for less money, while Hansson can service many more clients per year, and will therefore also end up better off.
The fact that Hansson might be working remotely from Marbella, Spain, while your office is in Des Moines, Iowa, doesn’t
matter to your company, as advances in communication and collaboration technology make the process near seamless. (This reality does matter, however, to the less-skilled local programmers living in Des Moines and in need of a steady paycheck.) This same trend holds for the growing number of fields where technology makes productive remote work possible—consulting, marketing, writing, design, and so on. Once the talent market is made universally accessible, those at the peak of the market thrive while the rest suffer.
In a seminal 1981 paper, the economist Sherwin Rosen worked out the mathematics behind these “winner-take-all” markets. One of his key insights was to explicitly model talent—labeled, innocuously, with the variable q in his formulas—as a factor with “imperfect substitution,” which Rosen explains as follows: “Hearing a succession of mediocre singers does not add up to a single outstanding performance.” In other words, talent is not a commodity you can buy in bulk and combine to reach the needed levels: There’s a premium to being the best. Therefore, if you’re in a marketplace where the consumer has access to all performers, and everyone’s q value is clear, the consumer will choose the very best. Even if the talent advantage of the best is small compared to the next rung down on the skill ladder, the superstars still win the bulk of the market.
In the 1980s, when Rosen studied this effect, he focused on examples like movie stars and musicians, where there existed clear markets, such as music stores and movie theaters, where an audience has access to different performers and can accurately approximate their talent before making a purchasing decision. The rapid rise of communication and collaboration technologies has transformed many other formerly local markets into a similarly universal bazaar. The small company looking for a computer programmer or public relations consultant now has access to an international marketplace of talent in the same way that the advent of the record store allowed the small-town music fan to bypass local musicians to buy albums from the world’s best bands. The superstar effect, in other words, has a broader application today than Rosen could have predicted thirty years ago. An increasing number of individuals in our economy are now competing with the rock stars of their sectors.
The Owners
Owners now spend money on machines and reducing labor. Only high profile people are working and can survive in market.
The question we must now face is the obvious one: How does one join these winners? At the risk of quelling your rising enthusiasm, I should first confess that I have no secret for quickly amassing capital and becoming the next John Doerr. (If I had such secrets, it’s unlikely I’d share them in a book.) The other two winning groups, however, are accessible. How to access them is the goal we tackle next.
How to become a winner in the New Economy?
Two Core Abilities for Thriving in the New Economy
- The ability to quickly master hard things.
- The ability to produce at an elite level, in terms of both quality and speed.
The two core abilities just described depend on your ability to perform deep work. If you haven’t mastered this foundational skill, you’ll struggle to learn hard things or produce at an elite level.